Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3678
Missing cells6713
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory578.4 B

Variable types

Categorical10
Text3
Numeric10

Alerts

store room is highly imbalanced (55.7%) Imbalance
facing has 1046 (28.4%) missing values Missing
super_built_up_area has 1802 (49.0%) missing values Missing
built_up_area has 1988 (54.1%) missing values Missing
carpet_area has 1806 (49.1%) missing values Missing
area is highly skewed (γ1 = 29.7350038) Skewed
built_up_area is highly skewed (γ1 = 40.70657243) Skewed
carpet_area is highly skewed (γ1 = 24.33323909) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 463 (12.6%) zeros Zeros

Reproduction

Analysis started2025-04-24 17:34:42.127495
Analysis finished2025-04-24 17:35:09.771433
Duration27.64 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.9 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowhouse
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2025-04-24T23:05:09.973020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:10.227479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15571
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size265.3 KiB
2025-04-24T23:05:10.658957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.871906
Min length1

Characters and Unicode

Total characters62038
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowbestech altura
2nd rowrof ananda
3rd rowindependent
4th rowumang monsoon breeze
5th rowtata primanti
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 167
 
1.7%
emaar 155
 
1.6%
global 154
 
1.6%
m3m 152
 
1.6%
signature 151
 
1.6%
heights 134
 
1.4%
Other values (783) 7498
77.5%
2025-04-24T23:05:11.551942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6711
 
10.8%
6006
 
9.7%
a 5864
 
9.5%
r 4172
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3721
 
6.0%
s 3473
 
5.6%
l 2945
 
4.7%
o 2756
 
4.4%
Other values (31) 18394
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55485
89.4%
Space Separator 6006
 
9.7%
Decimal Number 529
 
0.9%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6711
12.1%
a 5864
 
10.6%
r 4172
 
7.5%
n 4164
 
7.5%
i 3832
 
6.9%
t 3721
 
6.7%
s 3473
 
6.3%
l 2945
 
5.3%
o 2756
 
5.0%
d 2488
 
4.5%
Other values (16) 15359
27.7%
Decimal Number
ValueCountFrequency (%)
3 208
39.3%
2 82
 
15.5%
1 75
 
14.2%
6 57
 
10.8%
8 32
 
6.0%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6006
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55485
89.4%
Common 6553
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6711
12.1%
a 5864
 
10.6%
r 4172
 
7.5%
n 4164
 
7.5%
i 3832
 
6.9%
t 3721
 
6.7%
s 3473
 
6.3%
l 2945
 
5.3%
o 2756
 
5.0%
d 2488
 
4.5%
Other values (16) 15359
27.7%
Common
ValueCountFrequency (%)
6006
91.7%
3 208
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 57
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6711
 
10.8%
6006
 
9.7%
a 5864
 
9.5%
r 4172
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3721
 
6.0%
s 3473
 
5.6%
l 2945
 
4.7%
o 2756
 
4.4%
Other values (31) 18394
29.6%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
2025-04-24T23:05:12.014478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3183796
Min length3

Characters and Unicode

Total characters34273
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 79
2nd rowsector 95
3rd rowsector 9
4th rowsector 78
5th rowsector 72
ValueCountFrequency (%)
sector 3450
46.7%
road 177
 
2.4%
sohna 165
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (107) 2922
39.6%
2025-04-24T23:05:12.766713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3803
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3549
10.4%
c 3501
10.2%
t 3461
10.1%
1 1074
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6206
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23305
68.0%
Decimal Number 7261
 
21.2%
Space Separator 3707
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3803
16.3%
s 3694
15.9%
r 3694
15.9%
e 3549
15.2%
c 3501
15.0%
t 3461
14.9%
a 697
 
3.0%
d 248
 
1.1%
n 229
 
1.0%
h 202
 
0.9%
Other values (10) 227
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1074
14.8%
0 804
11.1%
8 780
10.7%
9 761
10.5%
6 739
10.2%
7 682
9.4%
2 679
9.4%
3 665
9.2%
5 593
8.2%
4 484
6.7%
Space Separator
ValueCountFrequency (%)
3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23305
68.0%
Common 10968
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3803
16.3%
s 3694
15.9%
r 3694
15.9%
e 3549
15.2%
c 3501
15.0%
t 3461
14.9%
a 697
 
3.0%
d 248
 
1.1%
n 229
 
1.0%
h 202
 
0.9%
Other values (10) 227
 
1.0%
Common
ValueCountFrequency (%)
3707
33.8%
1 1074
 
9.8%
0 804
 
7.3%
8 780
 
7.1%
9 761
 
6.9%
6 739
 
6.7%
7 682
 
6.2%
2 679
 
6.2%
3 665
 
6.1%
5 593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3803
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3549
10.4%
c 3501
10.2%
t 3461
10.1%
1 1074
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6206
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5334226
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:13.100205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9802521
Coefficient of variation (CV)1.1763739
Kurtosis14.938471
Mean2.5334226
Median Absolute Deviation (MAD)0.72
Skewness3.2797359
Sum9274.86
Variance8.8819024
MonotonicityNot monotonic
2025-04-24T23:05:13.416884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 64
 
1.7%
1.2 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3059
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13893.043
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:13.718908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16818
median9020
Q313888
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7070

Descriptive statistics

Standard deviation23206.907
Coefficient of variation (CV)1.6703978
Kurtosis186.97874
Mean13893.043
Median Absolute Deviation (MAD)2795
Skewness11.438687
Sum50862429
Variance5.3856054 × 108
MonotonicityNot monotonic
2025-04-24T23:05:14.001755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

Skewed 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2887.8375
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:14.353273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11230
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation23164.36
Coefficient of variation (CV)8.0213517
Kurtosis942.28627
Mean2887.8375
Median Absolute Deviation (MAD)533
Skewness29.735004
Sum10572373
Variance5.3658758 × 108
MonotonicityNot monotonic
2025-04-24T23:05:15.060508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3268
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size399.5 KiB
2025-04-24T23:05:15.629625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.231648
Min length12

Characters and Unicode

Total characters199464
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 2150(199.74 sq.m.)
2nd rowCarpet area: 728 (67.63 sq.m.)
3rd rowCarpet area: 33 (27.59 sq.m.)
4th rowBuilt Up area: 1730 (160.72 sq.m.)
5th rowSuper Built up area 2905(269.88 sq.m.)
ValueCountFrequency (%)
area 5574
18.5%
sq.m 3656
12.1%
up 3021
 
10.0%
built 2317
 
7.7%
super 1876
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8701
28.9%
2025-04-24T23:05:16.501214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9208
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82361
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82775
41.5%
Decimal Number 47144
23.6%
Space Separator 26469
 
13.3%
Other Punctuation 23409
 
11.7%
Uppercase Letter 8595
 
4.3%
Close Punctuation 5536
 
2.8%
Open Punctuation 5536
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13156
15.9%
r 9458
11.4%
e 9322
11.3%
s 7568
9.1%
q 7432
9.0%
t 7325
8.8%
u 6773
8.2%
p 6769
8.2%
m 5545
6.7%
l 3702
 
4.5%
Other values (5) 5725
6.9%
Decimal Number
ValueCountFrequency (%)
1 9208
19.5%
0 6631
14.1%
2 5688
12.1%
5 4714
10.0%
3 3961
8.4%
4 3712
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3158
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3021
35.1%
S 1876
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20392
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26469
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108094
54.2%
Latin 91370
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13156
14.4%
r 9458
10.4%
e 9322
10.2%
s 7568
8.3%
q 7432
8.1%
t 7325
8.0%
u 6773
7.4%
p 6769
7.4%
m 5545
 
6.1%
l 3702
 
4.1%
Other values (10) 14320
15.7%
Common
ValueCountFrequency (%)
26469
24.5%
. 20392
18.9%
1 9208
 
8.5%
0 6631
 
6.1%
2 5688
 
5.3%
) 5536
 
5.1%
( 5536
 
5.1%
5 4714
 
4.4%
3 3961
 
3.7%
4 3712
 
3.4%
Other values (5) 16247
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9208
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82361
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3597064
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:16.813296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8975034
Coefficient of variation (CV)0.5647825
Kurtosis18.215499
Mean3.3597064
Median Absolute Deviation (MAD)1
Skewness3.4853698
Sum12357
Variance3.600519
MonotonicityNot monotonic
2025-04-24T23:05:17.133177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 943
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 943
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4241436
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:17.416383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9479448
Coefficient of variation (CV)0.56888526
Kurtosis17.544566
Mean3.4241436
Median Absolute Deviation (MAD)1
Skewness3.2490529
Sum12594
Variance3.794489
MonotonicityNot monotonic
2025-04-24T23:05:17.698668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1048
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1048
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.5 KiB
3+
1172 
3
1074 
2
885 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3186514
Min length1

Characters and Unicode

Total characters4850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 885
24.1%
1 365
 
9.9%
0 182
 
4.9%

Length

2025-04-24T23:05:18.026953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:18.282830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 885
 
24.1%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
61.1%
2 885
 
24.1%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7966658
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:18.589272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0123965
Coefficient of variation (CV)0.88460971
Kurtosis4.5156476
Mean6.7966658
Median Absolute Deviation (MAD)3
Skewness1.6938301
Sum24869
Variance36.148912
MonotonicityNot monotonic
2025-04-24T23:05:18.872589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 352
 
9.6%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 352
9.6%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

Missing 

Distinct8
Distinct (%)0.3%
Missing1046
Missing (%)28.4%
Memory size229.5 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowNorth-West
3rd rowEast
4th rowSouth-East
5th rowSouth-West

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1046
28.4%

Length

2025-04-24T23:05:19.140300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:19.393194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size252.8 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.387167
Min length9

Characters and Unicode

Total characters49238
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Property
2nd rowNew Property
3rd rowUndefined
4th rowRelatively New
5th rowModerately Old

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 267
 
7.3%

Length

2025-04-24T23:05:19.701929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:19.969300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.3%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 267
 
3.8%
construction 267
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8432
17.1%
l 4721
 
9.6%
t 3639
 
7.4%
3372
 
6.8%
y 3105
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14075
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38816
78.8%
Uppercase Letter 7050
 
14.3%
Space Separator 3372
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8432
21.7%
l 4721
12.2%
t 3639
9.4%
y 3105
 
8.0%
r 2889
 
7.4%
d 2308
 
5.9%
w 2239
 
5.8%
i 2219
 
5.7%
a 2209
 
5.7%
o 1993
 
5.1%
Other values (7) 5062
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2239
31.8%
R 1646
23.3%
P 896
12.7%
O 866
 
12.3%
U 573
 
8.1%
M 563
 
8.0%
C 267
 
3.8%
Space Separator
ValueCountFrequency (%)
3372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45866
93.2%
Common 3372
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8432
18.4%
l 4721
 
10.3%
t 3639
 
7.9%
y 3105
 
6.8%
r 2889
 
6.3%
d 2308
 
5.0%
N 2239
 
4.9%
w 2239
 
4.9%
i 2219
 
4.8%
a 2209
 
4.8%
Other values (14) 11866
25.9%
Common
ValueCountFrequency (%)
3372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8432
17.1%
l 4721
 
9.6%
t 3639
 
7.4%
3372
 
6.8%
y 3105
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14075
28.6%

super_built_up_area
Real number (ℝ)

Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1924.7876
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:20.342708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11478.75
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)736.25

Descriptive statistics

Standard deviation764.21698
Coefficient of variation (CV)0.39703964
Kurtosis10.344048
Mean1924.7876
Median Absolute Deviation (MAD)372
Skewness1.8362017
Sum3610901.5
Variance584027.59
MonotonicityNot monotonic
2025-04-24T23:05:20.716938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1635
44.5%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1988
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:21.047036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2025-04-24T23:05:21.392979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1988
54.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1806
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:21.731149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-04-24T23:05:22.047311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1806
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2973 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Length

2025-04-24T23:05:22.321154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:22.557002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2025-04-24T23:05:22.843428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:23.133468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2025-04-24T23:05:23.396746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:23.646540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3022 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Length

2025-04-24T23:05:23.902025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:24.147050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2025-04-24T23:05:24.390742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:24.626441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2437 
1
1038 
2
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2437
66.3%
1 1038
28.2%
2 203
 
5.5%

Length

2025-04-24T23:05:24.846483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T23:05:25.106870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2437
66.3%
1 1038
28.2%
2 203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2437
66.3%
1 1038
28.2%
2 203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2437
66.3%
1 1038
28.2%
2 203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2437
66.3%
1 1038
28.2%
2 203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2437
66.3%
1 1038
28.2%
2 203
 
5.5%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.493475
Minimum0
Maximum174
Zeros463
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-04-24T23:05:25.384334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.06497
Coefficient of variation (CV)0.74223515
Kurtosis-0.88015164
Mean71.493475
Median Absolute Deviation (MAD)38
Skewness0.45920248
Sum262953
Variance2815.891
MonotonicityNot monotonic
2025-04-24T23:05:25.707316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 463
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
165 55
 
1.5%
38 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2313
62.9%
ValueCountFrequency (%)
0 463
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-04-24T23:05:05.717634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:44.200551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:47.261900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:49.530446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:51.857725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:54.274928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:56.655013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:58.829290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:01.380533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:03.584443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:05.924078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:44.596490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:47.479810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:49.737717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:52.076947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:54.524115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:56.864427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:59.040842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:01.635009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:03.779056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:06.147470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:44.943636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:47.696225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:49.947212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:52.404089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:54.760164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:57.074749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:59.266553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:01.848728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:04.008088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:06.334432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:45.285495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:47.887413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:50.190782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:52.631390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:54.965135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:57.273088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:59.488114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:02.044399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:04.224313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:06.578562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:45.654489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:48.106368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:50.440114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:52.875569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:55.194315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:57.513296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:00.054710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:02.322926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:04.459528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:06.805621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:46.028196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:48.378886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:50.731855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:53.090992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:55.432279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:57.741208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:00.291578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:02.553184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:04.673918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:07.022487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:46.343105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:48.607020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:50.963409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:53.308008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:55.662844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:57.957035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:00.516456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:02.763065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:04.886223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:07.223979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:46.601714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:48.842145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:51.181039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:53.515202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:55.877817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:58.198647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:00.721213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:02.936807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:05.090088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:07.452379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:46.843525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:49.088167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:51.408328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:53.782130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:56.157715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:58.402728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:00.952876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:03.158015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:05.282760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:07.662571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:47.039872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:49.306191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:51.637713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:54.026047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:56.408170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:04:58.606446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:01.156788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:03.336629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-24T23:05:05.489390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2025-04-24T23:05:08.018760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-24T23:05:08.745732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-24T23:05:09.425360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatbestech alturasector 791.858604.02150.0Super Built up area 2150(199.74 sq.m.)343+11.0NorthNew Property2150.0NaNNaN01000049
1flatrof anandasector 950.466318.0728.0Carpet area: 728 (67.63 sq.m.)22211.0North-WestNew PropertyNaNNaN728.010000082
2houseindependentsector 90.227407.0297.0Carpet area: 33 (27.59 sq.m.)2221.0NaNUndefinedNaNNaN33.00000000
3flatumang monsoon breezesector 780.955491.01730.0Built Up area: 1730 (160.72 sq.m.)3329.0EastRelatively NewNaN1730.0NaN00000165
4flattata primantisector 724.0013769.02905.0Super Built up area 2905(269.88 sq.m.)4527.0South-EastModerately Old2905.0NaNNaN01000038
5houseemaar mgf marbellasector 669.0021251.04235.0Plot area 5605(520.72 sq.m.)Built Up area: 5200 sq.ft. (483.1 sq.m.)Carpet area: 4235 sq.ft. (393.44 sq.m.)443+NaNSouth-WestRelatively NewNaN5200.04235.0011101114
6flatbestech park view residencysector 21.027208.01415.0Super Built up area 1415(131.46 sq.m.)22311.0South-WestModerately Old1415.0NaNNaN00000092
7flatdlf regency parksector 281.3511739.01150.0Super Built up area 1150(106.84 sq.m.)Built Up area: 1120 sq.ft. (104.05 sq.m.)Carpet area: 1050 sq.ft. (97.55 sq.m.)22214.0EastOld Property1150.01120.01050.000100066
8flatindiabulls enigmasector 1103.4510147.03400.0Super Built up area 3400(315.87 sq.m.)4439.0NorthModerately Old3400.0NaNNaN010001125
9houseinternational city by sobha phase 2sector 1097.0012963.05400.0Plot area 600(501.68 sq.m.)5622.0WestRelatively NewNaN5400.0NaN010000154
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793houseindependentsector 414.2526235.01620.0Plot area 180(150.5 sq.m.)1093+4.0NorthModerately OldNaN1620.0NaN11000136
3794flatsmart world orchardsector 611.7015000.01133.0Carpet area: 1150 (106.84 sq.m.)2224.0NaNUnder ConstructionNaNNaN1150.000000024
3795flatats kocoonsector 1092.2210596.02095.0Super Built up area 2095(194.63 sq.m.)33320.0EastRelatively New2095.0NaNNaN01001079
3796flatthe close northsector 502.7512500.02200.0Super Built up area 2605(242.01 sq.m.)Built Up area: 2400 sq.ft. (222.97 sq.m.)Carpet area: 2200 sq.ft. (204.39 sq.m.)3333.0South-EastModerately Old2605.02400.02200.0010000110
3797flatemaar mgf the palm drivesector 661.5115130.0998.0Super Built up area 1200(111.48 sq.m.)Built Up area: 1188 sq.ft. (110.37 sq.m.)Carpet area: 998 sq.ft. (92.72 sq.m.)2225.0NorthRelatively New1200.01188.0998.000010182
3798flatm3m skycitysector 652.1016030.01310.0Super Built up area 1310(121.7 sq.m.)22243.0NaNNew Property1310.0NaNNaN000000127
3799flatats tourmalinesector 1091.758139.02150.0Super Built up area 2150(199.74 sq.m.)343+9.0North-EastRelatively New2150.0NaNNaN01000094
3800flatshree vardhman victoriasector 701.108461.01300.0Carpet area: 1300 (120.77 sq.m.)2228.0North-EastRelatively NewNaNNaN1300.0000000101
3801flatvatika inxt floorssector 82a0.757500.01000.0Built Up area: 1000 (92.9 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)2221.0NaNUndefinedNaN1000.0650.00000000
3802flatorris aster court premiersector 851.505859.02560.0Super Built up area 2560(237.83 sq.m.)Built Up area: 2017 sq.ft. (187.39 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)453+6.0WestNew Property2560.02017.01800.001000128